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- Tasks: Deliver innovative data-driven insights and enhance econometric models in real estate.
- Company: Leading property and research firm with a focus on data science.
- Benefits: Fixed-term contract, central London office, and collaborative work environment.
- Other info: Great opportunity for career growth and collaboration across diverse teams.
- Why this job: Join a dynamic team and make an impact in the real estate sector.
- Qualifications: Experience in econometrics, statistics, and data science techniques.
The predicted salary is between 40000 - 50000 £ per year.
Be part of a team delivering innovative data-driven insight and analysis within a leading property and research environment. An exciting opportunity has arisen for an enthusiastic and innovative Econometrician/Data Scientist to join a market-leading Modelling and Intelligence team. The successful candidate will work on research projects focused on real estate modelling, forecasting, and analytical insight generation.
The role will involve enhancing existing econometric and statistical models, while also developing new analytical projects in collaboration with stakeholders across multiple real estate sectors. This is a research-oriented position ideally suited to candidates with experience applying econometric, statistical, and data science techniques to solve commercial business problems.
Based in a central London office, the role plays a key part in delivering analytical insight and strategic intelligence to internal stakeholders and clients across an international network. Reporting to the Head of Data Science, the position is offered on a fixed-term contract basis. Candidates should be comfortable working independently while also collaborating closely with cross-functional analytics and research teams. Prior experience within the real estate sector would be advantageous, although not essential.
Operating Environment
The role sits within the Modelling and Intelligence function as part of a wider Global Research division. Day-to-day management will be provided by the Head of Data Science.
Technical Responsibilities
- Collaborate with colleagues across Data Science, Data Engineering, Geospatial, and Innovation teams to deliver econometric and data science analysis relating to real estate trends and market behaviour.
- Apply advanced statistical and econometric techniques to complex datasets.
- Strong Python capability is essential.
- Experience with Databricks and Azure DevOps is highly desirable, though not mandatory.
- Manipulate, cleanse, and analyse large proprietary and external datasets.
- Produce regular analytical outputs, dashboards, reports, and data books for internal stakeholders.
- Clearly communicate findings and insights to both technical and non-technical audiences.
Statistician employer: Norton Blake
Contact Detail:
Norton Blake Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Statistician
✨Tip Number 1
Network like a pro! Reach out to professionals in the real estate and data science sectors on LinkedIn. Join relevant groups, attend webinars, and don’t be shy about asking for informational interviews. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your econometric and statistical projects. Use GitHub or a personal website to display your work. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills. Be ready to discuss your experience with Python, Databricks, and Azure DevOps. Practise explaining complex concepts in simple terms, as you'll need to communicate findings to both technical and non-technical audiences.
✨Tip Number 4
Don’t forget to apply through our website! We love seeing candidates who are genuinely interested in joining our team. Tailor your application to highlight your relevant experience and enthusiasm for the role. Let’s get you that interview!
We think you need these skills to ace Statistician
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with econometric and statistical techniques. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects or achievements!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re excited about this opportunity and how your background in data science can contribute to our Modelling and Intelligence team. Keep it engaging and personal!
Showcase Your Technical Skills: Since strong Python capability is essential, make sure to mention any relevant projects or experiences where you've used Python or other statistical tools. We love seeing practical examples of your work!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy!
How to prepare for a job interview at Norton Blake
✨Know Your Stats
Brush up on your econometric and statistical techniques before the interview. Be ready to discuss how you've applied these methods in past projects, especially in relation to real estate or similar sectors. This will show your potential employer that you can hit the ground running.
✨Showcase Your Python Skills
Since strong Python capability is essential for this role, prepare to demonstrate your coding skills. You might be asked to solve a problem or explain a project where you used Python. Have examples ready that highlight your ability to manipulate and analyse data effectively.
✨Communicate Clearly
You’ll need to convey complex findings to both technical and non-technical audiences. Practice explaining your previous work in simple terms. This will help you stand out as someone who can bridge the gap between data science and business insights.
✨Collaborate and Connect
This role involves working closely with various teams, so be prepared to discuss your experience in collaborative environments. Share examples of how you’ve worked with cross-functional teams to deliver successful projects, highlighting your teamwork and communication skills.